# A comparative analysis of video vision transformers on word-level sign language datasets

## TL;DR

This paper compares video vision transformers for recognizing Bangla Sign Language signs, showing they outperform traditional methods with high accuracy on small and large datasets.

## Contribution

The study introduces a comparative analysis of video transformers on Bangla Sign Language datasets, including novel benchmarking and evaluation techniques.

## Key findings

- VideoMAE achieved 96.9% accuracy on the BdSLW60 dataset with corrected frame rates.
- VideoMAE also reached 81.04% accuracy on front-facing signs in the larger BdSLW401 dataset.
- Video transformers outperformed traditional machine learning and deep learning approaches.

## Abstract

Sign Language Recognition (SLR) involves the automatic identification and classification of sign gestures from images or video, converting them into text or speech to improve accessibility for the hard-of-hearing community. In Bangladesh, Bangla Sign Language (BdSL) serves as the primary mode of communication for many individuals with hearing loss. This study fine-tunes state-of-the-art video transformer architectures VideoMAE, ViViT, and TimeSformer on BdSLW60, a small-scale BdSL dataset with 60 frequent signs. We standardized the videos to 30 FPS, resulting in 9,307 user trial clips. To evaluate scalability and robustness, the models were also fine-tuned on BdSLW401, a large-scale dataset with 401 sign classes. Additionally, we benchmark performance against public datasets, including LSA64 and WLASL. Data augmentation techniques such as random cropping, horizontal flipping, and short-side scaling were applied to improve model robustness. To ensure balanced evaluation across folds during model selection, we employed 10-fold stratified cross-validation on the training set of the BdSLW60 dataset, while signer-independent evaluation was carried out using held-out test data from unseen users U4 and U8. Results show that video transformer models significantly outperform traditional machine learning and deep learning approaches. Performance is influenced by factors such as dataset size, signer appearance, frame distribution, frame rate, and model architecture. Among the models, the VideoMAE variant (MCG-NJU/videomae-base-finetuned-kinetics) achieved the highest accuracies 96.9% on the frame rate corrected BdSLW60 dataset and 81.04% on the front-facing signs of BdSLW401 demonstrating strong potential for scalable and accurate BdSL recognition.